# Adversarial Training for Deep Learning-based Intrusion Detection Systems

arXiv: https://arxiv.org/abs/2104.09852 · HTML: https://mlsanity.com/paper/2104.09852/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 20 Apr 2021.

## Builds on (most cited first, 7 of 7)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks](https://mlsanity.com/paper/1905.05137/) (2019)
- [Efficient Intrusion Detection Using Evidence Theory](https://mlsanity.com/paper/2103.08585/) (2021)

## Built on by (most cited first, 6 of 6)

- [Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2303.06664/) (2023)
- [Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review](https://mlsanity.com/paper/2112.03315/) (2021)
- [TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems](https://mlsanity.com/paper/2210.15700/) (2022)
- [Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems](https://mlsanity.com/paper/2112.12095/) (2021)
- [Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems](https://mlsanity.com/paper/2207.04843/) (2022)
- [Targeted Adversarial Traffic Generation : Black-box Approach to Evade Intrusion Detection Systems in IoT Networks](https://mlsanity.com/paper/2603.23438/) (2026)

## Abstract (arXiv)

Nowadays, Deep Neural Networks (DNNs) report state-of-the-art results in many machine learning areas, including intrusion detection. Nevertheless, recent studies in computer vision have shown that DNNs can be vulnerable to adversarial attacks that are capable of deceiving them into misclassification by injecting specially crafted data. In security-critical areas, such attacks can cause serious damage; therefore, in this paper, we examine the effect of adversarial attacks on deep learning-based intrusion detection. In addition, we investigate the effectiveness of adversarial training as a defense against such attacks. Experimental results show that with sufficient distortion, adversarial examples are able to mislead the detector and that the use of adversarial training can improve the robustness of intrusion detection.